




"""simple docstring"""





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


A				=							logging.get_logger(__name__)
A				=							{"""vocab_file""": """spiece.model"""}

A				=							{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

A				=							{
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}



class   a__					(						__magic_name__ ):
			lowercase_		=						VOCAB_FILES_NAMES
			lowercase_		=						PRETRAINED_VOCAB_FILES_MAP
			lowercase_		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase_		=						["input_ids", "attention_mask"]


			def __init__(   self   :     Dict   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Union[str, Any]=False   ,	UpperCamelCase_   :     Dict=False   ,	UpperCamelCase_   :     List[Any]=False   ,	UpperCamelCase_   :     Tuple=None   ,	UpperCamelCase_   :     str=None   ,	UpperCamelCase_   :     Dict=None   ,	UpperCamelCase_   :     Tuple=None   ,	UpperCamelCase_   :     Optional[Dict[str, Any]] = None   ,	**UpperCamelCase_   :     Optional[Any]   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       {} if sp_model_kwargs is None else sp_model_kwargs

						__UpperCAmelCase						:			Dict												=       kwargs.get("name_or_path")
						if name_or_path is None:
									logger.warning(
									    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
									    " you are testing the model, this can safely be ignored")
									__UpperCAmelCase						:			Optional[int]												=       "None"

						# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
						__UpperCAmelCase						:			str												=       "<|endoftext|>" if eos_token is None else eos_token
						__UpperCAmelCase						:			Optional[Any]												=       "<unk>" if unk_token is None else unk_token
						if "gpt-sw3-7b" in name_or_path:
									__UpperCAmelCase						:			Dict												=       unk_token if pad_token is None else pad_token
									__UpperCAmelCase						:			Any												=       eos_token if bos_token is None else bos_token
						else:
									__UpperCAmelCase						:			List[Any]												=       "<pad>" if pad_token is None else pad_token
									__UpperCAmelCase						:			List[str]												=       "<s>" if bos_token is None else bos_token

						super().__init__(
						    do_lower_case=UpperCamelCase_   ,	remove_space=UpperCamelCase_   ,	keep_accents=UpperCamelCase_   ,	bos_token=UpperCamelCase_   ,	eos_token=UpperCamelCase_   ,	unk_token=UpperCamelCase_   ,	pad_token=UpperCamelCase_   ,	sp_model_kwargs=self.sp_model_kwargs   ,	**UpperCamelCase_   ,	)

						__UpperCAmelCase						:			Optional[int]												=       do_lower_case
						__UpperCAmelCase						:			List[str]												=       remove_space
						__UpperCAmelCase						:			Any												=       keep_accents
						__UpperCAmelCase						:			int												=       vocab_file

						__UpperCAmelCase						:			List[str]												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(UpperCamelCase_)

						# Used for whitespace normalization in input texts
						# fmt : off
						__UpperCAmelCase						:			int												=       {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
						# fmt : on

						# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
						__UpperCAmelCase						:			Dict												=       re.compile(
						    F"[{''.join(map(UpperCamelCase_   ,	list(range(0   ,	9)) + list(range(11   ,	32)) + list(range(127   ,	160)) + [160, 173, 8203]))}]")


			def __getstate__(   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.__dict__.copy()
						__UpperCAmelCase						:			Optional[Any]												=       None
						return state


			def __setstate__(   self   :     Union[str, Any]   ,	UpperCamelCase_   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       d

						# for backward compatibility
						if not hasattr(self   ,	"sp_model_kwargs"):
									__UpperCAmelCase						:			Optional[Any]												=       {}

						__UpperCAmelCase						:			Tuple												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(self.vocab_file)


			@property
			# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
			def    a_  (   self   :     Any):


						"""simple docstring"""




						return len(self.sp_model)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       self.non_printing_characters_re.sub(""   ,	UpperCamelCase_)

						# Normalize whitespaces
						__UpperCAmelCase						:			List[Any]												=       "".join([char if char not in self.whitespaces else " " for char in text])

						# NFC Unicode normalization
						__UpperCAmelCase						:			Any												=       unicodedata.normalize("NFC"   ,	UpperCamelCase_)
						return text


			def    a_  (   self   :     Dict   ,	UpperCamelCase_   :     str   ,	**UpperCamelCase_   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       self.preprocess_text(UpperCamelCase_)
						return self.sp_model.encode(UpperCamelCase_   ,	out_type=UpperCamelCase_)


			def    a_  (   self   :     Union[str, Any]   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						return self.sp_model.PieceToId(UpperCamelCase_)


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						return self.sp_model.IdToPiece(UpperCamelCase_)


			@staticmethod
			def    a_  (   UpperCamelCase_   :     str):


						"""simple docstring"""




						return out_string


			def    a_  (   self   :     int   ,	UpperCamelCase_   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       []
						__UpperCAmelCase						:			Tuple												=       ""
						__UpperCAmelCase						:			Any												=       False
						for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
												# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
												if not prev_is_special:
															out_string += " "

												out_string += self.sp_model.decode(UpperCamelCase_) + token
												__UpperCAmelCase						:			int												=       True
												__UpperCAmelCase						:			Dict												=       []
									else:
												current_sub_tokens.append(UpperCamelCase_)
												__UpperCAmelCase						:			List[Any]												=       False
						out_string += self.sp_model.decode(UpperCamelCase_)

						return out_string


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
						vocab.update(self.added_tokens_encoder)
						return vocab


			def    a_  (   self   :     Union[str, Any]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Optional[str] = None):


						"""simple docstring"""




						if not os.path.isdir(UpperCamelCase_):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
						__UpperCAmelCase						:			List[str]												=       os.path.join(
						    UpperCamelCase_   ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

						if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
									copyfile(self.vocab_file   ,	UpperCamelCase_)
						elif not os.path.isfile(self.vocab_file):
									with open(UpperCamelCase_   ,	"wb") as fi:
												__UpperCAmelCase						:			Optional[int]												=       self.sp_model.serialized_model_proto()
												fi.write(UpperCamelCase_)

						return (out_vocab_file,)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     Union[str, List[str]]   ,	UpperCamelCase_   :     Union[str, bool] = False):


						"""simple docstring"""




						if isinstance(UpperCamelCase_   ,	UpperCamelCase_):
									__UpperCAmelCase						:			Any												=       self.preprocess_text(UpperCamelCase_)
									__UpperCAmelCase						:			Any												=       self.sp_model.encode(UpperCamelCase_)
						else:
									__UpperCAmelCase						:			Dict												=       [self.preprocess_text(UpperCamelCase_) for t in text]
									__UpperCAmelCase						:			List[Any]												=       self.sp_model.encode(UpperCamelCase_)

						if return_tensors is True or return_tensors == "pt":
									__UpperCAmelCase						:			Optional[Any]												=       torch.tensor(UpperCamelCase_)

						return token_ids


			def    a_  (   self   :     int   ,	UpperCamelCase_   :     Union[int, List[int]]):


						"""simple docstring"""




						return self.sp_model.decode(UpperCamelCase_)


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     "Conversation"):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       [F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
						__UpperCAmelCase						:			Optional[Any]												=       (
						    F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(UpperCamelCase_) + F"{self.bos_token}Bot:"
						)
						return self.encode(text=UpperCamelCase_)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




